Automated arthropod detection system

1. An automated arthropod detection system, comprising: one or more image sensors mounted on a powered arm; a communication unit, wherein said communication unit comprises at least one from the group of a light projector, a speaker, a display, and combinations thereof; and a processor, wherein the processor is configured to perform operations comprising: generating a sequence of movements to be executed at a plurality of future points in time by the powered arm; executing each movement in the sequence at the plurality of future points in time using the powered arm while also capturing a first one or more digital images of a subject or substrate from one or more positions using the one or more image sensors mounted on the powered arm; processing the first one or more digital images using an arthropod recognition machine learning algorithm; and communicating the results from the arthropod recognition machine learning algorithm to a user of the system using the communication unit.

2. The automated arthropod detection system of claim 1 , wherein: the operation of generating the sequence of movements to be executed at the plurality of future points in time by the powered arm comprises: capturing a second one or more digital images of the subject or substrate using the one or more image sensors mounted on the powered arm; and processing the second one or more digital images of the subject or substrate using a movement planning machine learning algorithm, wherein: said movement planning machine learning algorithm is a machine learning algorithm that accepts the second one or more digital images of the subject or substrate as inputs and produces outputs that represent the sequence of movements to be executed at the plurality of future points in time by the powered arm, the movements of which will to allow the first one or more image sensors mounted on the powered arm to capture the first one or more digital images of the subject or substrate in such a way that said first one or more digital images of the subject or substrate include a plurality of the subject or substrate's surfaces.

3. The automated arthropod detection system of claim 2 , further comprising a support, wherein the powered arm is mounted on the support.

4. The automated arthropod detection system of claim 2 , wherein: the communication unit comprises the light projector.

5. A method of arthropod detection using an automated arthropod detection system, the method comprising: generating a sequence of movements to be executed at a plurality of future points in time by a powered arm; executing each movement in the sequence at the plurality of future points in time while also capturing a first one or more digital images of a subject or substrate from one or more positions using one or more image sensors mounted on the powered arm; processing the first one or more digital images of the subject or substrate using an arthropod recognition machine learning algorithm; and communicating the results from the arthropod recognition machine learning algorithm to a user of the system using a communication unit comprising at least one from the group of a light projector, a speaker, a display, and combinations thereof.

6. The method of claim 5 , wherein the step of generating the sequence of movements to be executed at the plurality of future points in time by the powered arm comprises: capturing a second one or more digital images of the subject or substrate using the one or more image sensors mounted on the powered arm; and processing the second one or more digital images of the subject or substrate using a movement planning machine learning algorithm, and wherein: said movement planning machine learning algorithm is a machine learning algorithm that accepts the second one or more digital images of the subject or substrate as inputs and produces outputs that represent the sequence of movements to be executed at the plurality of future points in time by the powered arm, wherein said sequence of movements will allow the one or more image sensor sensors mounted on the powered arm to capture the first one or more digital images of the subject or substrate in such a way that said first one or more digital images of the subject or substrate include a plurality of the subject or substrate's surfaces; the movement planning machine learning algorithm is one of: a supervised learning method, an unsupervised learning method, a semi-supervised learning method, a reinforcement learning method, an optimization technique, a convolutional neural network, a recurrent neural network, a deep learning model, or a generative-adversarial network; and the parameters of the movement planning machine learning algorithm are chosen by training on one or more of first labeled or first unlabeled data.

7. The method of claim 6 , wherein: the arthropod recognition machine learning algorithm is a machine learning algorithm that accepts the first one or more digital images of the subject or substrate as inputs and generates outputs that consist of one or more numeric values that represent predictions about any arthropods that are present in the first one or more digital images of the subject or substrate; said arthropod recognition machine learning algorithm is one of: a supervised learning method, an unsupervised learning method, a semi-supervised learning method, a reinforcement learning method, an optimization technique, a convolutional neural network, a recurrent neural network, a deep learning model, or a generative-adversarial network; and the parameters of the arthropod recognition machine learning algorithm are chosen by training on one or more of second labeled or second unlabeled data.

8. The method of claim 7 , wherein predictions about any arthropods that are present in the first one or more digital images of the subject or substrate include one or more of: whether any arthropods are present in the first one or more digital images of the subject or substrate, the locations of any arthropods that are present in the first one or more digital images of the subject or substrate, or the species, age, sex, and/or morphological characteristics of one or more arthropods that are present in the first one or more digital images of the subject or substrate.

9. The method of claim 6 , wherein: the communication unit comprises the speaker; and the step of communicating the results from the arthropod recognition machine learning algorithm to the user of the system additionally comprises using the speaker to play sounds when an arthropod is detected.

10. The method of claim 6 , wherein the step of communicating the results from the arthropod recognition machine learning algorithm to the user of the system additionally comprises projecting one or more light beams from the light projector onto one or more regions of the subject or substrate where an arthropod has been detected.

11. The method of claim 6 , additionally comprising: eliciting feedback data from the user of the system after communicating the results from the arthropod recognition machine learning algorithm; and using that feedback data to improve the arthropod recognition machine learning algorithm.

12. The method of claim 6 , additionally comprising: repeating one or more of the steps of the method one or more times.

13. The method of claim 6 , wherein: the sequence of movements represented by the output of the movement planning machine learning algorithm will additionally cause the powered arm to exert force on and reposition one or more parts of the subject or substrate, or one or more parts of an object that is near the subject or substrate; said sequence of movements will additionally allow the one or more image sensors mounted on the powered arm to capture the first one or more digital images of the subject or substrate in such a way that said first one or more digital images of the subject or substrate include surfaces of the subject or substrate that were not previously visible; and said powered arm comprises a first powered arm and a second powered arm, wherein: the first powered arm is used to capture the first one or more digital images of the subject or substrate, and the second powered arm is used to exert force on and reposition one or more parts of the subject or substrate, or one or more parts of an object that is near the subject or substrate.

14. The method of claim 5 , wherein the arthropod recognition machine learning algorithm is a convolutional neural network that accepts the first one or more digital images of the subject or substrate as an input and generates numeric outputs that indicate whether one or more ticks is present in the first one or more digital images of the subject or substrate.